Search Results (213)

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the deep learning algorithms of residual and channel attention with the physical constraints of vertical modes of T/S profiles decomposed by empirical orthogonal functions (EOFs). The results from a series of single point experiments show that FFPG-net outperforms the CNN or CNN-PG (without physical guidance or feature fusion) in the reconstruction of subsurface T/S in a region of the South China Sea (SCS), with monthly mean RMSEs of 0.31 °C (0.35 °C) and 0.06 psu (0.07 psu) for the reconstructed T/S profiles in winter (summer), averaged over the water depth of 1200 m and the study area. In addition, the performance of the FFPG-net can be improved significantly by incorporating full surface currents or geostrophic currents derived from SSH into the input variables for training the neural network. The preliminary application of FFPG-net in the SCS using satellite-derived sea surface observations indicates that FFPG-net is reliable and feasible for reconstructing subsurface ocean thermal fields in real situations. Our study highlights the advantages and necessity of combining deep learning algorithms with physical constraints in reconstructing subsurface T/S profiles. It provides an effective tool for reconstructing the subsurface global ocean from remote-sensing sea surface observations in the future. Full article

Regional rapid forecasting of vertical ocean temperature profiles is increasingly important for marine aquaculture, as these profiles directly affect habitat management and the physiological responses of farmed species. However, observational temperature profile data with sufficient temporal resolution are often unavailable, limiting their use in regional rapid forecasting. In addition, traditional numerical ocean models suffer from intensive computational demands and limited operational flexibility, making them unsuitable for regional rapid forecasting applications. To address this gap, we propose PICA-Net (Physics-Inspired CNN–Attention–BiLSTM Network), a hybrid deep learning model that coordinates large-scale satellite observations with local-scale, continuous in situ data to enhance predictive fidelity. The model also incorporates weak physical constraints during training that enforce temporal–spatial diffusion consistency, mixed-layer homogeneity, and surface heat flux consistency, enhancing physical consistency and interpretability. The model uses hourly historical inputs to predict temperature profiles at 6 h intervals over a period of 24 h, incorporating features such as sea surface temperature, sea surface height anomalies, wind fields, salinity, ocean currents, and net heat flux. Experimental results demonstrate that PICA-Net outperforms baseline models in terms of accuracy and generalization. Additionally, its lightweight design enables real-time deployment on edge devices, offering a viable solution for localized, on-site forecasting in smart aquaculture. Full article

Maritime corrosion is a global problem often retarded through protective coatings containing corrosion inhibitors (CIs). ZnAl layered double hydroxides (LDH) have been used to immobilize CIs, which can reduce their early leaching and, thus, foster long-term corrosion protection. However, the environmental behavior of these nanomaterials remains largely unknown, particularly in the context of global changes. The present study aims to assess the environmental behavior of four anti-corrosion nanomaterials in an ocean acidification scenario (IPCC SSP3-7.0). Three different concentrations of the nanostructured CIs (1.23, 11.11, and 100 mg L⁻¹) were prepared and maintained at 20 °C and 30 °C in artificial salt water (ASW) at two pH values, with and without the presence of organic matter. The nanomaterials’ particle size and the release profiles of Al³⁺, Zn²⁺, and anions were monitored over time. In all conditions, the hydrodynamic size of the dispersed nanomaterials confirmed that the high ionic strength favors their aggregation/agglomeration. In the presence of organic matter, dissolved Al³⁺ increased, while Zn²⁺ decreased, and increased in the ocean acidification scenario at both temperatures. CIs were more released in the presence of humic acid. These findings demonstrate the influence of the tested parameters in the nanomaterials’ environmental behavior, leading to the release of metals and CIs. Full article

Seaweed is a sustainable ingredient that has been suggested to improve the nutritional aspects as well as the sensory properties of different food products. The objective of this study was to evaluate the flavor properties of extracts from brown seaweed (Ascophyllum nodosum) and sugar kelp (Saccharina latissimi) obtained at different temperatures. These varieties commonly grow in the Atlantic Ocean. The seaweed samples were extracted using water at three different temperatures (50 °C, 70 °C, and 90 °C). The volatile fraction of the extracts was extracted with headspace solid-phase microextraction and analyzed by gas chromatography–mass spectrometry. The headspace chemical composition varies significantly among seaweed extracts and at different extraction temperatures. Major classes of identified compounds were aldehydes, ketones, alcohols, hydrocarbons, and halogenated compounds. Extracts were also evaluated using temporal check-all-that-apply (with 84 untrained participants). The different temperatures had minimal impact on the flavour properties of the brown seaweed samples, but the extraction temperature did influence the properties of the sugar kelp samples. Increasing the extraction temperature seemed to lead to an increase in bitterness, savouriness, and earthy flavor, but future studies are needed to confirm this finding. This study continues the exploration of the flavor properties of seaweeds and identifies the dynamic flavor profile of brown seaweed and sugar kelp under different extraction conditions. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

The atmospheric boundary layer (ABL) and tropopause play critical roles in weather formation and climate change. This study initially focuses on the ABL height (ABLH), tropopause height (TPH), and temperature (TPT) retrieved from the integrated radio occultation (RO) profiles from FY-3E, FY-3F, and FY-3G satellites during September 2022 to August 2024. All three FY-3 series satellites are equipped with the RO payload of Global Navigation Satellite System Radio Occultation Sounder-II (GNOS-II), which includes open-loop tracking RO observations from the BeiDou navigation satellite system (BDS) and the Global Positioning System (GPS). The wavelet covariance transform method was used to determine the ABL top, and the temperature lapse rate was applied to judge the tropopause. Results show that the maximum ABL detection rate of FY-3/GNOS-II RO can reach up to 76% in the subtropical eastern Pacific, southern hemisphere Atlantic, and eastern Indian Ocean. The ABLH is highly consistent with the collocated radiosonde observations and presents distinct seasonal variations. The TPH retrieved from FY-3/GNOS-II RO profiles is in agreement with the radiosonde-derived TPH, and both TPH and TPT from RO profiles display well-defined spatial structures. From 45°S to 45°N and south of 55°S, the annual cycle of the TPT is negatively correlated with the TPH. This study substantiates the promising performance of FY-3/GNOS-II RO measurements in observing the ABL and tropopause, which can be incorporated into the weather and climate systems. Full article

In this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can predict that a buoy is about to enter a vortex, we can automatically adjust the buoy’s sampling frequency to better observe the vortex’s structure and development. To address this requirement, based on the profile data, including latitude and longitude, temperature, and salinity, collected by 56 buoys in the Arctic Ocean from 2014 to 2023, this paper uses the TSMixer time-series model to predict whether an ocean buoy is about to be inside a vortex. The TSMixer model effectively captures the spatio-temporal characteristics of multivariate time series through time-mixing and feature-mixing mechanisms, and the accuracy of the model reaches 84.6%. The proposed model is computationally efficient and has a low memory footprint, which is suitable for real-time applications and provides accurate prediction support for marine monitoring. Full article

The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article

In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, and evaluating their impacts on ecosystems. While previous studies have largely concentrated on the effects of various modeling approaches on reconstructing oceanic variables, limited attention has been paid to the role of surface waves in reconstruction. This study, based on sea surface data, employs a deep learning-based neural network model, U-Net, to reconstruct 3D temperature and salinity across the North Pacific and Equatorial Pacific within the upper 200 m. The input of wave information includes the significant wave height (SWH), Langmuir number (La), and Langmuir enhancement factor (ε); the latter two indicate the strength of Langmuir turbulence, which promotes vertical mixing in the ocean surface layer and thereby affects profiles of temperature and salinity. The results indicate that incorporating wave information, particularly the La and ε, significantly enhances the model’s ability to reconstruct ocean temperature and salinity. This highlights the critical role of surface waves in enhancing the reconstruction of 3D ocean temperature and salinity. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article

Copolymers of N-isopropylacrylamide (NIPA) and alkyl acrylic acids that swell and shrink in response to pH were prepared by dispersion polymerization at 35 °C using N-isopropylacrylamide (transduction monomer), methylenebisacrylamide (crosslinker), 2-dimethoxy-2-phenyl-acetophenone (initiator), N-tert-butylacrylamide (transition temperature modifier), and acrylic acid, methacrylic acid, ethacrylic acid, and propacrylic acid (functional comonomer). The diameter of the microspheres of the copolymer varied between 0.5 µm and 1.0 µm. These microspheres were cast into hydrogel membranes prepared by mixing the pH-sensitive swellable polymer particles with aqueous polyvinyl alcohol solutions followed by crosslinking the polyvinyl alcohol with glutaric dialdehyde for use as pH sensors. Large changes in the turbidity of the polyvinyl alcohol membrane monitored using a Cary 6000 UV–visible absorbance spectrometer were observed as the pH of the buffer solution in contact with the membrane was varied. Polymer swelling was reversible for many of these NIPA-based copolymers. The buffer capacity, ionic strength, pH, and temperature of the buffer solution in contact with the membrane were systematically varied to provide an in-depth pH profile of each copolymer. A unique aspect of this study was the investigation of the response of the NIPA-based polymers to changes in the pH of the solution in contact with the membrane at low buffer concentrations (0.5 mM). The response rate and the reversibility of polymer swelling even at low buffer capacity suggest that NIPA-based copolymers can be coupled to an optical fiber for pH sensing in the environment. We envision using these polymers to monitor rising acidity levels in the ocean due to water that has become enriched in carbon dioxide that endangers shell-building organisms by reducing the amount of carbonate available to them. Full article

Brillouin scattering lidar is a potential remote sensing technique for measuring the distribution profiles of temperature and salinity in the upper ocean. To realize high-precision simultaneous inversion of temperature and salinity in seawater, we propose a solution tailored for the measurement of temperature–salinity profiles. Three distinct models with error correction are discussed based on dual-wavelength, dual-angle, and dual-parameter approaches, respectively. We analyze the accuracy of these three inversion models using the least squares method based on the actual temperature and salinity data of World Ocean Atlas 2023 (WOA23). The results show that the average temperature and salinity errors for the dual-wavelength model are 0.009 °C and 0.001‰, for the dual-angle model are 0.13 °C and 0.30‰, and for the dual-parameter model are 0.03 °C and 0.08‰. And on this basis, we inverse the temperature and salinity of 0–200 m upper seawater in the South China Sea by employing the dual-wavelength model with the average inversion errors of 0.05 °C and 0.02‰, respectively. The findings presented in this work hold significant importance for the application of Brillouin lidar in remote sensing the distribution of temperature and salinity in ocean. Full article